Small Object Detection with Multiscale Features by Guo X. Hu, Zhong Yang, Lei Hu, Li Huang, Jia M. Han

“…The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. …”
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Multimodal Data Fusion for Depression Detection Approach by Mariia Nykoniuk, Oleh Basystiuk, Nataliya Shakhovska, Nataliia Melnykova

“…These networks were developed using convolutional neural network (CNN) layers to learn local patterns, a bidirectional LSTM (Bi-LSTM) to process sequences, and a self-attention mechanism to improve focus on key parts of the data. …”
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Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram by Xu Zhang, Xi Hui, Pengwu Wan, Tengfei Hui, Xiongfei Li

“…Then, the feature parameters of this 2D image are extracted by radial basis function neural network (RBFNN) to complete the recognition of the modulation mode of the input signal. …”
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Industrial Robot Vibration Anomaly Detection Based on Sliding Window One-Dimensional Convolution Autoencoder by ZhiDan Zhong, Yao Zhao, AoYu Yang, HaoBo Zhang, DongHao Qiao, ZhiHui Zhang

“…First, the convolutional neural network and the autoencoder model are effectively integrated to construct a one-dimensional convolutional autoencoder model. …”
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An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning by Néstor Rodríguez-Padial, Marta Marín, Rosario Domingo

“…In the proposed Custom Balance Scorecard design, an exploratory data phase is integrated with another analysis and prediction phase using Principal Component Analysis algorithms and Machine Learning that uses Artificial Neural Network algorithms. This new extension allows better control over the maintenance function of an industrial plant in the medium-term with a yearly horizon taken over monthly intervals which allows the measurement of the indicators of strategic productive areas and the discovery of hidden behavior patterns in work orders. …”
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SQL Injection Detection Based on Lightweight Multi-Head Self-Attention by Rui-Teng Lo, Wen-Jyi Hwang, Tsung-Ming Tai

“…This paper presents a novel neural network model for the detection of Structured Query Language (SQL) injection attacks for web applications. …”
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Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy by Huihui Li, Linfeng Gou, Hua Zheng, Huacong Li

“…A new intelligent fault diagnosis scheme combining improved pattern gradient spectrum entropy (IPGSE) and convolutional neural network (CNN) is proposed in this paper, aiming at the problem of poor fault diagnosis effect and real-time performance when CNN directly processes one-dimensional time series signals of aeroengine. …”
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Prediction and design optimization of mechanical properties for rubber fertilizer hose reinforced with helically wrapped nylon by Mengfan Wang, Lixin Zhang, Changxin Fu

“…For the first time, the Crested Porcupine Optimizer algorithm was used to improve the Generalized Regression Neural Network (CPO-GRNN) method to establish a surrogate model for predicting the mechanical properties of HWNR hoses, and it was compared with Response Surface Methodology (RSM). …”
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A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images by Jiang Xie, Jinzhu Wei, Huachan Shi, Zhe Lin, Jinsong Lu, Xueqing Zhang, Caifeng Wan

“…In this study, a novel convolutional neural network model with bimodal layer-wise feature fusion module (BLFFM) and temporal hybrid attention module (THAM) is proposed, which uses multistage bimodal ultrasound images as input for early prediction of the efficacy of neoadjuvant chemotherapy in locally advanced breast cancer (LABC) patients. …”
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CNN-Based Pupil Center Detection for Wearable Gaze Estimation System by Warapon Chinsatit, Takeshi Saitoh

“…This paper presents a convolutional neural network- (CNN-) based pupil center detection method for a wearable gaze estimation system using infrared eye images. …”
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Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods by Pragya Kashyap, Kalbhavi Vadhi Raj, Jyoti Sharma, Naveen Dutt, Pankaj Yadav

“…Next, benchmarking was performed across six different supervised-classification algorithms viz. logistic-regression, naïve-bayes, random-forest, extreme-gradient-boost (XGBoost), k-nearest neighbor, and deep neural network. Noteworthy, XGBoost, with an accuracy of 76.25%, and AUROC (area-under-receiver-operating-characteristic) of 0.81 with 69% specificity and 76% sensitivity, outperform the other five classification algorithms using LDA-transformed features. …”
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Displacement-Based Back-Analysis of the Model Parameters of the Nuozhadu High Earth-Rockfill Dam by Yongkang Wu, Huina Yuan, Bingyin Zhang, Zongliang Zhang, Yuzhen Yu

“…In this method, an artificial neural network is used as a substitute for time-consuming finite element analysis, and an evolutionary algorithm is applied for both network training and parameter optimization. …”
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An improved quantitative assessment method on hazardous interference of power lines to the signal cable in high‐speed railway by Chang Liu, Shiwu Yang, Yong Cui, Shaotong Chu, Qihui Xiong

“…Abstract High‐speed railway (HSR) presents the characteristics of a heavy load, large traction current, and ballastless track‐bed. As the 'neural network' of the signalling system, the line‐side signal cable may threaten both human safety and control information transmission for an HSR operation when interfered with by a strong traction current. …”
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Automatic MRI Image Classification Using Attention and Residual CNNs With Enhanced Image Denoising Filters by Suganya Athisayamani, A. Robert Singh, Faten Khalid Karim, Samih M. Mostafa

“…The enhanced image features are used for classification with three different CNN classification models: Convolutional Neural Network (CNN), CNN with attention module (CNN-AM) and CNN with a residual module (CNN-RM). …”
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Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision by Min Wang, Yanwen Zhang, Huiping Ye

“…By combining two independent Lyapunov functions and radial basis function (RBF) neural network (NN) approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. …”
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Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data by Ahmad Hasasneh, Nikolas Kampel, Praveen Sripad, N. Jon Shah, Jürgen Dammers

“…We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. …”
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2.5D Facial Personality Prediction Based on Deep Learning by Jia Xu, Weijian Tian, Guoyun Lv, Shiya Liu, Yangyu Fan

“…Our experimental results show that the deep neural network trained by large labeled datasets can reliably predict people’s multidimensional personality characteristics through 2.5D static facial contour images, and the prediction accuracy is better than the previous method using 2D images.…”
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SVDD: SAR Vehicle Dataset Construction and Detection by Dan Gao, Xiaofang Wu, Zhijin Wen, Yue Xu, Zhengchao Chen

“…With the advent of high-quality SAR images and the rapid development of computing technology, the object detection algorithms based on convolution neural network have attracted a lot of attention in the field of SAR object detection. …”
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Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning by Yi Du, Jie Chen, Ting Zhang

“…Hence, a method for reconstructing porous media is presented by applying DTL to extract features from a training image (TI) of porous media to replace the process of scanning a TI for different patterns as in multiple-point statistical methods. The deep neural network is practically used to extract the complex features from the TI of porous media, and then, a reconstructed result can be obtained by transfer learning through copying these features. …”
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Validation of the New Algorithm for Rain Rate Retrieval from AMSR2 Data Using TMI Rain Rate Product by Elizaveta Zabolotskikh, Bertrand Chapron

“…AMSR2 brightness temperature differences at C- and X-band channels are then used as inputs to train a neural network (NN) function for RR retrieval. Validation is performed against Tropical Rain Measurement Mission (TRMM) Microwave Instrument (TMI) RR products. …”
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